An electronic sensor (CCD or CMOS) is commonly used in digital still and video cameras for converting an optical signal into a digital representation of the image in the optical signal. The digital image, as measured at each pixel, is not exactly proportional to the amount of light incident on the sensor as a variety of factors contribute noise to the measured value. The sensor noise can be broken down into two components, a fixed pattern noise (FPN) and non-fixed noise.
Fixed pattern noise is noise that depends on the pixel location (spatial dependency) as well as the amount of light incident on the pixel (signal dependency). The noise is substantially invariant over short periods (i.e., almost unchanged over a few seconds). Fixed pattern noise is commonly a function of temperature and so will change as the sensor heats up or cools down. Furthermore, fixed pattern noise changes very slowly as the sensor ages (i.e., over months or years).
Non-fixed noise is noise that has no statistical dependence from picture to picture. The noise is naturally created within the sensor and associated analog electronics. Stray background light and radiation also contribute to the non-fixed noise.
Black frame subtraction is a conventional technique used to cancel fixed pattern noise. The black frame subtraction technique captures a picture with the camera shutter closed. Since no optical signal is incident on the sensor while the shutter is closed, a resulting black frame records only noise. The black frame is then subtracted from future pictures to cancel the fixed pattern noise.
Referring to FIG. 1, a diagram of a conventional device 20 that uses black-frame subtraction is shown. Light incident on a sensor 22 is converted into a raw picture signal. A black frame stored in a memory 24 is subtracted from the raw picture signal by an image processor 26. After further conventional processing, a processed picture signal is presented by the image processor 26.
Conventional black frame subtraction techniques have several drawbacks. For example, black frame subtraction can only eliminate signal independent (additive) fixed pattern noise because the black frame only measures noise when no light is incident on the sensor. The black frame subtraction techniques cannot reduce signal dependent noise. Furthermore, the black frame itself is typically large. For example, an 8 million pixel sensor using 12 bits/pixel creates a 12 megabyte black frame. Therefore, storing the black frame is usually expensive. The storage is typically implemented in a large, high-bandwidth memory. The bandwidth criterion is particularly high for high-speed high-resolution still cameras and video cameras (typically processing 60 pictures/second). Furthermore, the time used to compute the black frame (i.e., capture and process the black frame) reduces the usability of the camera. The black-frame capture can take the form of increased start-up time if done when the camera is first powered up or increased picture-to-picture time if done after the picture is taken. If the black-frame capture is done when the camera is first powered up, the sensor will typically be cool, but as the camera is used the sensor will heat up thus changing the noise pattern. Fixed pattern noise models and bad pixel masks measured at low temperatures may be quite inaccurate if used at high temperatures.